Spectra often consist of hundreds to many thousands of intensity measurements as a function of wavelength or scattering angle. This information can be used to identify and quantitate multiple target species in complex matrices of samples of interest, which may include flowing streams (e.g., flow cytometry) or any sort of microscopy, whether full field imaging or confocal, including scanning and Nipkow disk.
Spectral analysis to identify and analyze target species from a sample is usually performed by sending the light from a sample through a spectrometer or monochromator which disperses the photon signal onto a detector. Each target generates a unique spectrum that is then received by the detector and measured. Examples of commercial instruments using this approach include Raman gas analyzers, NIR gas analyzers, flow cytometers, fluorescence microscopes, Raman imaging microscopes, and particle size analyzers. In some cases, the monomchromator can be replaced by discrete bandpass filters, as in the case of fluorescent probes that have been designed to fluoresce at discrete wavelengths.
In many cases, the desired analytes spectra may overlap, as in the case of many biological probes such as fluorescent probes widely used in molecular biology, fluorescence microscopy and flow cytometry. In these cases, the problem is how to increase the number of analytes (probes) and still be able to separate them spectrally for both detection and quantitation. One approach has been to acquire the complete spectrum of the system and then deconvolve the summed spectrum of each analyte; such techniques are particularly useful for fluorescent biological probes as fluorescence adds linearly. This means that the measured spectrum is the linear sum of each analyte spectrum times its concentration. Linear unmixing can deconvolve the measured spectrum to recover whether and how much of each analyte is present. This is done offline, after acquisition of the complete spectrum. This approach can also be applied to images as well. This technique has been demonstrated to separate, without cross talk, fluorescent probes that differ by only 5 nm in their peaks. The spectra also can be analyzed using powerful multivariate mathematical methods. These methods can provide accurate quantitative determinations of multiple target species in similar cases when the spectral differences between species in the sample are too subtle for successful analysis using bandpass filters, dichroics, longpass and shortpass filters or combinations of such devices.
For some probes or analytes, the signals are separated enough spectrally that discrete bandpass filters can be used, as in commercially available fluorescence microscopy filter cubes. A bandpass filter transmits light that is characteristic of the target species, and not produced by the other components of the sample. More generally though, many desired probes such as gene reporters overlap significantly spectrally. Also, by increasing the number of probes to increase the number of analytes per measurement, the spectral signatures would inherently get closer and begin to overlap. Since the desired spectral signals in these cases are so close, discrete optical bandpass filters cannot separate the analytes without crosstalk that degrades quantitation and classification of particular analytes. The limitation of this approach is that the entire spectrum has to be acquired and then subjected to mathematical analysis. Analysis rates are limited by the time to readout the data from the detector and then perform the mathematical analysis.
Thus, a need exists for a system and method for continuously identifying and analyzing multiple target species in real time by utilizing all the analytically useful information in the spectrum.